Skip to main content
Springer Nature Link
Log in

MINLIP: Efficient Learning of Transformation Models

  • Conference paper
Artificial Neural Networks – ICANN 2009 (ICANN 2009)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 5768))

Included in the following conference series:

Abstract

This paper studies a risk minimization approach to estimate a transformation model from noisy observations. It is argued that transformation models are a natural candidate to study ranking models and ordinal regression in a context of machine learning. We do implement a structural risk minimization strategy based on a Lipschitz smoothness condition of the transformation model. Then, it is shown how the estimate can be obtained efficiently by solving a convex quadratic program with O(n) linear constraints and unknowns, with n the number of data points. A set of experiments do support these findings.

KP is a postdoctoral researcher with FWO Flanders (A 4/5 SB 18605). S. Van Huffel is a full professor and J.A.K. Suykens is a professor at the Katholieke Universiteit Leuven, Belgium. This research is supported by GOA-AMBioRICS, CoE EF/05/006, FWO G.0407.02 and G.0302.07, IWT, IUAP P6/04, eTUMOUR (FP6-2002-LIFESCIHEALTH 503094).

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Agarwal, S., Graepel, T., Herbrich, R., Har-Peled, S., Roth, D.: Generalization bounds for the area under the ROC curve. Journal of Machine Learning Research 6, 393–425 (2005)

    MathSciNet  MATH  Google Scholar 

  2. Breese, J.S., Heckerman, D., Kadie, C.: Empirical Analysis of Predictive Algorithms for Collaborative Filtering. In: Proceedings of the 14th Conference on Uncertainty in Artificial Intelligence, pp. 43–52 (1998)

    Google Scholar 

  3. Cheng, S.C., Wei, L.J., Ying, Z.: Predicting Survival Probabilities with Semiparametric Transformation Models. Journal of the American Statistical Association 92(437), 227–235 (1997)

    Article  MathSciNet  MATH  Google Scholar 

  4. Chu, W., Keerthi, S.S.: New approaches to support vector ordinal regression. In: ICML, pp. 145–152 (2005)

    Google Scholar 

  5. Clémençon, S., Lugosi, G., Vayatis, N.: Ranking and Scoring Using Empirical Risk Minimization. In: Auer, P., Meir, R. (eds.) COLT 2005. LNCS (LNAI), vol. 3559, pp. 1–15. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  6. Dabrowska, D.M., Doksum, K.A.: Partial likelihood in transformation models with censored data. Scandinavian Journal of Statistics 15(1), 1–23 (1988)

    MathSciNet  MATH  Google Scholar 

  7. Herbrich, R., Graepel, T., Obermayer, K.: Large margin rank boundaries for ordinal regression. In: Advances in Large Margin Classifiers, pp. 115–132. MIT Press, Cambridge (2000)

    Google Scholar 

  8. Kalbfleisch, J.D., Prentice, R.L.: The Statistical Analysis of Failure Time Data. Wiley series in probability and statistics. Wiley, Chichester (2002)

    Book  MATH  Google Scholar 

  9. Koenker, R., Geling, O.: Reappraising Medfly Longevity: A Quantile Regression Survival Analysis. Journal of the American Statistical Association 96(454), 458–468 (2001)

    Article  MathSciNet  MATH  Google Scholar 

  10. Pelckmans, K., Suykens, J.A.K., De Moor, B.: A Risk Minimization Principle for a Class of Parzen Estimators. In: Advances in Neural Information Processing Systems 20, pp. 1–8. MIT Press, Cambridge (2008)

    Google Scholar 

  11. Shawe-Taylor, J., Cristianini, N.: Kernel Methods for Pattern Analysis. Cambridge University Press, Cambridge (2004)

    Book  MATH  Google Scholar 

  12. Suykens, J.A.K., Van Gestel, T., De Brabanter, J., De Moor, B., Vandewalle, J.: Least Squares Support Vector Machines. World Scientific, Singapore (2002)

    Book  MATH  Google Scholar 

  13. Van Belle, V., Pelckmans, K., Suykens, J.A.K., Vanhuffel, S.: Support Vector Machines for Survival Analysis. In: Proceedings of the Third International Conference on Computational Intelligence in Medicine and Healthcare, CIMED, Plymouth, UK, July 25-27, pp. 1–6 (2007)

    Google Scholar 

  14. Vapnik, V.N.: Statistical Learning Theory. Wiley and Sons, Chichester (1998)

    MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Katholieke Universiteit Leuven, ESAT-SCD, Kasteelpark Arenberg 10, B-3001, Leuven, Belgium

    Vanya Van Belle, Kristiaan Pelckmans, Johan A. K. Suykens & Sabine Van Huffel

Authors
  1. Vanya Van Belle
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Kristiaan Pelckmans
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Johan A. K. Suykens
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Sabine Van Huffel
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Dipartimento di Elettronica, Politecnico di Milano, Piazza L. da Vinci 32, 20133, Milano, Italy

    Cesare Alippi

  2. Department of Electrical and Computer Engineering, University of Cyprus, 75 Kallipoleos Street, 1678, Nicosia, Cyprus

    Marios Polycarpou , Christos Panayiotou  & Georgios Ellinas ,  & 

Rights and permissions

Reprints and permissions

Copyright information

© 2009 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Van Belle, V., Pelckmans, K., Suykens, J.A.K., Van Huffel, S. (2009). MINLIP: Efficient Learning of Transformation Models. In: Alippi, C., Polycarpou, M., Panayiotou, C., Ellinas, G. (eds) Artificial Neural Networks – ICANN 2009. ICANN 2009. Lecture Notes in Computer Science, vol 5768. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04274-4_7

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-04274-4_7

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-04273-7

  • Online ISBN: 978-3-642-04274-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation